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Tomography (Ann Arbor, Mich.)|May 26, 2026
Conditional Diffusion Models for CT Image Synthesis from CBCT: A Systematic ReviewAlzahra Altalib, Chunhui Li, Alessandro PerelliMedical Physics|August 4, 2026
Equivariant conditional diffusion model for head and neck CT image synthesis from CBCTAlzahra Altalib, Chunhui Li, Alessandro PerelliFrontiers in Bioengineering and Biotechnology|October 29, 2024
VP-net: an end-to-end deep learning network for elastic wave velocity prediction in human skin in vivo using optical coherence elastographyYilong Zhang, Jinpeng Liao, Zhengshuyi Feng, et al.Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences|May 10, 2021
Regularization by denoising sub-sampled Newton method for spectral CT multi-material decompositionAlessandro Perelli, Martin S AndersenEuropean Heart Journal. Imaging Methods and Practice|March 25, 2026
Deep learning for cardiac MRI: performance evidence and barriers to clinical integration. A Systematic Review and Meta-AnalysisFatemah Aladwani, Alessandro Perelli, Ify Mordi, et al.Physics in Medicine and Biology|June 23, 2022
LRR-CED: low-resolution reconstruction-aware convolutional encoder-decoder network for direct sparse-view CT image reconstructionV S S Kandarpa, Alessandro Perelli, Alexandre Bousse, et al.IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control|July 14, 2015
Compressive sensing of full wave field data for structural health monitoring applicationsTommaso Di Ianni, Luca De Marchi, Alessandro Perelli, et al.Physics in Medicine and Biology|November 8, 2018
Quantitative cone-beam CT reconstruction with polyenergetic scatter model fusionJonathan H Mason, Alessandro Perelli, William H Nailon, et al.Physics in Medicine and Biology|October 6, 2017
Polyquant CT: direct electron and mass density reconstruction from a single polyenergetic sourceJonathan H Mason, Alessandro Perelli, William H Nailon, et al.Physics in Medicine and Biology|February 3, 2025
MLAR-UNet: LDCT image denoising based on U-Net with multiple lightweight attention-based modules and residual reinforcementHao Tang, Ningfeng Que, Yanwen Tian, et al.Pageof 45